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    Estimates of Root-Mean-Square Random Error for Finite Samples of Estimated Precipitation

    Source: Journal of Applied Meteorology:;1997:;volume( 036 ):;issue: 009::page 1191
    Author:
    Huffman, George J.
    DOI: 10.1175/1520-0450(1997)036<1191:EORMSR>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: The random errors contained in a finite set E of precipitation estimates result from both finite sampling and measurement?algorithm effects. The expected root-mean-square random error associated with the estimated average precipitation in E is shown to be σr = r?[(H ? p)/pNI]1/2, where r? is the space?time-average precipitation estimate over E, H is a function of the shape of the probability distribution of precipitation (the nondimensional second moment), p is the frequency of nonzero precipitation in E, and NI is the number of independent samples in E. All of these quantities are variables of the space?time-average dataset. In practice H is nearly constant and close to the value 1.5 over most of the globe. An approximate form of σr is derived that accommodates the limitations of typical monthly datasets, then it is applied to the microwave, infrared, and gauge precipitation monthly datasets from the Global Precipitation Climatology Project. As an aid to visualizing differences in σr for various datasets, a ?quality index? is introduced. Calibration in a few locations with dense gauge networks reveals that the approximate form is a reasonable first step in estimating σr.
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      Estimates of Root-Mean-Square Random Error for Finite Samples of Estimated Precipitation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4147875
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    contributor authorHuffman, George J.
    date accessioned2017-06-09T14:06:23Z
    date available2017-06-09T14:06:23Z
    date copyright1997/09/01
    date issued1997
    identifier issn0894-8763
    identifier otherams-12526.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4147875
    description abstractThe random errors contained in a finite set E of precipitation estimates result from both finite sampling and measurement?algorithm effects. The expected root-mean-square random error associated with the estimated average precipitation in E is shown to be σr = r?[(H ? p)/pNI]1/2, where r? is the space?time-average precipitation estimate over E, H is a function of the shape of the probability distribution of precipitation (the nondimensional second moment), p is the frequency of nonzero precipitation in E, and NI is the number of independent samples in E. All of these quantities are variables of the space?time-average dataset. In practice H is nearly constant and close to the value 1.5 over most of the globe. An approximate form of σr is derived that accommodates the limitations of typical monthly datasets, then it is applied to the microwave, infrared, and gauge precipitation monthly datasets from the Global Precipitation Climatology Project. As an aid to visualizing differences in σr for various datasets, a ?quality index? is introduced. Calibration in a few locations with dense gauge networks reveals that the approximate form is a reasonable first step in estimating σr.
    publisherAmerican Meteorological Society
    titleEstimates of Root-Mean-Square Random Error for Finite Samples of Estimated Precipitation
    typeJournal Paper
    journal volume36
    journal issue9
    journal titleJournal of Applied Meteorology
    identifier doi10.1175/1520-0450(1997)036<1191:EORMSR>2.0.CO;2
    journal fristpage1191
    journal lastpage1201
    treeJournal of Applied Meteorology:;1997:;volume( 036 ):;issue: 009
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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